ABOUT THIS COURSE

Azure OpenAI Service provides access to OpenAI's powerful large language models such as GPT; the model behind the popular ChatGPT service. These models enable various natural language processing (NLP) solutions to understand, converse, and generate content. In this course, you'll learn how to provision Azure OpenAI service, deploy models, and use them in generative AI applications.

Full TIme

This course is designed for software developers and data scientists who need to use large language models for generative AI. It is also suitable for project managers who are exploring the use of generative AI in the workplace. Although there are no prerequisites required to join this course, familiarity with Azure and Python will facilitate a better learning experience.

This course will be valuable to anyone seeking to understand how the Azure OpenAI service can be used to implement generative AI solutions.

1 Day

7 Hours

Module 1: Get started with Azure OpenAI Service

This module provides engineers with the skills to begin building an Azure OpenAI Service solution.

Learning objectives

By the end of this module, you'll be able to:

• Create an Azure OpenAI Service resource and understand types of Azure OpenAI base models.
• Use the Azure OpenAI Studio, console, or REST API to deploy a base model and test it in the Studio's playgrounds.
• Generate completions to prompts and begin to manage model parameters.

Module 2: Build natural language solutions with Azure OpenAI Service

This module provides engineers with the skills to begin building apps that integrate with the Azure OpenAI Service.

Learning objectives

By the end of this module, you'll be able to:

• Integrate Azure OpenAI into your application
• Differentiate between different endpoints available to your application
• Generate completions to prompts using the REST API and language specific SDKs

Module 3: Apply prompt engineering with Azure OpenAI Service

Prompt engineering in Azure OpenAI is a technique that involves designing prompts for natural language processing models. This process improves accuracy and relevancy in responses, optimizing the performance of the model.

Learning objectives

By the end of this module, you'll be able to:

• Understand the concept of prompt engineering and its role in optimizing Azure OpenAI models' performance.
• Know how to design and optimize prompts to better utilize AI models.
• Include clear instructions, request output composition, and use contextual content to improve the quality of the model's responses.

Module 4: Generate code with Azure OpenAI Service

This module shows engineers how to use the Azure OpenAI Service to generate and improve code.

Learning objectives

By the end of this module, you'll be able to:

• Use natural language prompts to write code
• Build unit tests and understand complex code with AI models
• Generate comments and documentation for existing code

Module 5: Generate images with Azure OpenAI Service

The Azure OpenAI service includes the DALL-E model, which you can use to generate original images based on natural language prompts.

Learning objectives

By the end of this module, you'll be able to:

• Describe the capabilities of DALL-E in the Azure openAI service
• Use the DALL-E playground in Azure OpenAI Studio
• Use the Azure OpenAI REST interface to integrate DALL-E image generation into your apps

Module 6: Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service

Azure OpenAI on your data allows developers to implement RAG with supported AI chat models to reference specific sources of data to ground the response.

Learning objectives

By the end of this module, you'll be able to:

• Describe the capabilities of Azure OpenAI on your data
• Configure Azure OpenAI to use your own data
• Use Azure OpenAI API to generate responses based on your own data